#install.packages(“rgdal”) #install.packages(“pryr”) #install.packages(“rgeos”) #install.packages(“htmlwidgets”) #install.packages(“leaflet”)
# create a directory where the shapefiles are
dir <- paste0(getwd(),"/data_to_use")
file_list <- list.files(dir, pattern="*.shp$", full.names=TRUE)
file_list## [1] "/Users/Franciscorowe 1/Dropbox/Francisco/Research/in_progress/EU paper/interactive_map/EU_map/data_to_use/AUT_2010_99.shp"
## [2] "/Users/Franciscorowe 1/Dropbox/Francisco/Research/in_progress/EU paper/interactive_map/EU_map/data_to_use/BEL_2005_589_2.shp"
## [3] "/Users/Franciscorowe 1/Dropbox/Francisco/Research/in_progress/EU paper/interactive_map/EU_map/data_to_use/BRL_2011_118.shp"
## [4] "/Users/Franciscorowe 1/Dropbox/Francisco/Research/in_progress/EU paper/interactive_map/EU_map/data_to_use/BUL_2011_263.shp"
## [5] "/Users/Franciscorowe 1/Dropbox/Francisco/Research/in_progress/EU paper/interactive_map/EU_map/data_to_use/CHE_2000_184.shp"
## [6] "/Users/Franciscorowe 1/Dropbox/Francisco/Research/in_progress/EU paper/interactive_map/EU_map/data_to_use/CZE_2010_77.shp"
## [7] "/Users/Franciscorowe 1/Dropbox/Francisco/Research/in_progress/EU paper/interactive_map/EU_map/data_to_use/DNK_2011_99_2.shp"
## [8] "/Users/Franciscorowe 1/Dropbox/Francisco/Research/in_progress/EU paper/interactive_map/EU_map/data_to_use/EST_2016_223.shp"
## [9] "/Users/Franciscorowe 1/Dropbox/Francisco/Research/in_progress/EU paper/interactive_map/EU_map/data_to_use/FIN_2011_336.shp"
## [10] "/Users/Franciscorowe 1/Dropbox/Francisco/Research/in_progress/EU paper/interactive_map/EU_map/data_to_use/GER_2009_412.shp"
## [11] "/Users/Franciscorowe 1/Dropbox/Francisco/Research/in_progress/EU paper/interactive_map/EU_map/data_to_use/GRE_2011_54.shp"
## [12] "/Users/Franciscorowe 1/Dropbox/Francisco/Research/in_progress/EU paper/interactive_map/EU_map/data_to_use/HUN_2010_168.shp"
## [13] "/Users/Franciscorowe 1/Dropbox/Francisco/Research/in_progress/EU paper/interactive_map/EU_map/data_to_use/ITA_2009_107.shp"
## [14] "/Users/Franciscorowe 1/Dropbox/Francisco/Research/in_progress/EU paper/interactive_map/EU_map/data_to_use/LTU_2010_58.shp"
## [15] "/Users/Franciscorowe 1/Dropbox/Francisco/Research/in_progress/EU paper/interactive_map/EU_map/data_to_use/NLD_2010_431_2.shp"
## [16] "/Users/Franciscorowe 1/Dropbox/Francisco/Research/in_progress/EU paper/interactive_map/EU_map/data_to_use/NOR_2013_428.shp"
## [17] "/Users/Franciscorowe 1/Dropbox/Francisco/Research/in_progress/EU paper/interactive_map/EU_map/data_to_use/POL_2010_376_2.shp"
## [18] "/Users/Franciscorowe 1/Dropbox/Francisco/Research/in_progress/EU paper/interactive_map/EU_map/data_to_use/POR_2011_30.shp"
## [19] "/Users/Franciscorowe 1/Dropbox/Francisco/Research/in_progress/EU paper/interactive_map/EU_map/data_to_use/ROU_2002_42.shp"
## [20] "/Users/Franciscorowe 1/Dropbox/Francisco/Research/in_progress/EU paper/interactive_map/EU_map/data_to_use/RUS_2010_80_2.shp"
## [21] "/Users/Franciscorowe 1/Dropbox/Francisco/Research/in_progress/EU paper/interactive_map/EU_map/data_to_use/SPA_2011_52.shp"
## [22] "/Users/Franciscorowe 1/Dropbox/Francisco/Research/in_progress/EU paper/interactive_map/EU_map/data_to_use/SWE_2012_290.shp"
## [23] "/Users/Franciscorowe 1/Dropbox/Francisco/Research/in_progress/EU paper/interactive_map/EU_map/data_to_use/TUR_2012_81.shp"
## [24] "/Users/Franciscorowe 1/Dropbox/Francisco/Research/in_progress/EU paper/interactive_map/EU_map/data_to_use/UK_2011_404.shp"library(rgdal)## Loading required package: sp## rgdal: version: 1.4-6, (SVN revision 841)
## Geospatial Data Abstraction Library extensions to R successfully loaded
## Loaded GDAL runtime: GDAL 2.4.2, released 2019/06/28
## Path to GDAL shared files: /Library/Frameworks/R.framework/Versions/3.6/Resources/library/rgdal/gdal
## GDAL binary built with GEOS: FALSE
## Loaded PROJ.4 runtime: Rel. 5.2.0, September 15th, 2018, [PJ_VERSION: 520]
## Path to PROJ.4 shared files: /Library/Frameworks/R.framework/Versions/3.6/Resources/library/rgdal/proj
## Linking to sp version: 1.3-1AUT <- readOGR(dsn = "data_to_use",
layer = tools::file_path_sans_ext(basename(file_list[1])))## OGR data source with driver: ESRI Shapefile
## Source: "/Users/Franciscorowe 1/Dropbox/Francisco/Research/in_progress/EU paper/interactive_map/EU_map/data_to_use", layer: "AUT_2010_99"
## with 99 features
## It has 7 fieldsBEL <- readOGR(dsn = "data_to_use",
layer = tools::file_path_sans_ext(basename(file_list[2])))## OGR data source with driver: ESRI Shapefile
## Source: "/Users/Franciscorowe 1/Dropbox/Francisco/Research/in_progress/EU paper/interactive_map/EU_map/data_to_use", layer: "BEL_2005_589_2"
## with 589 features
## It has 8 fields
## Integer64 fields read as strings: 9_3_popBRL <- readOGR(dsn = "data_to_use",
layer = tools::file_path_sans_ext(basename(file_list[3])))## OGR data source with driver: ESRI Shapefile
## Source: "/Users/Franciscorowe 1/Dropbox/Francisco/Research/in_progress/EU paper/interactive_map/EU_map/data_to_use", layer: "BRL_2011_118"
## with 118 features
## It has 20 fields
## Integer64 fields read as strings: 1_popBUL <- readOGR(dsn = "data_to_use",
layer = tools::file_path_sans_ext(basename(file_list[4])))## OGR data source with driver: ESRI Shapefile
## Source: "/Users/Franciscorowe 1/Dropbox/Francisco/Research/in_progress/EU paper/interactive_map/EU_map/data_to_use", layer: "BUL_2011_263"
## with 263 features
## It has 15 fields
## Integer64 fields read as strings: ID_0 ID_1 ID_2CHE <- readOGR(dsn = "data_to_use",
layer = tools::file_path_sans_ext(basename(file_list[5])))## OGR data source with driver: ESRI Shapefile
## Source: "/Users/Franciscorowe 1/Dropbox/Francisco/Research/in_progress/EU paper/interactive_map/EU_map/data_to_use", layer: "CHE_2000_184"
## with 184 features
## It has 13 fields
## Integer64 fields read as strings: 2_popCZE <- readOGR(dsn = "data_to_use",
layer = tools::file_path_sans_ext(basename(file_list[6])))## OGR data source with driver: ESRI Shapefile
## Source: "/Users/Franciscorowe 1/Dropbox/Francisco/Research/in_progress/EU paper/interactive_map/EU_map/data_to_use", layer: "CZE_2010_77"
## with 77 features
## It has 7 fieldsDNK <- readOGR(dsn = "data_to_use",
layer = tools::file_path_sans_ext(basename(file_list[7])))## OGR data source with driver: ESRI Shapefile
## Source: "/Users/Franciscorowe 1/Dropbox/Francisco/Research/in_progress/EU paper/interactive_map/EU_map/data_to_use", layer: "DNK_2011_99_2"
## with 99 features
## It has 7 fields
## Integer64 fields read as strings: 4_popEST <- readOGR(dsn = "data_to_use",
layer = tools::file_path_sans_ext(basename(file_list[8])))## OGR data source with driver: ESRI Shapefile
## Source: "/Users/Franciscorowe 1/Dropbox/Francisco/Research/in_progress/EU paper/interactive_map/EU_map/data_to_use", layer: "EST_2016_223"
## with 223 features
## It has 16 fields
## Integer64 fields read as strings: ID_0 ID_1 ID_2 7_2v_popFIN <- readOGR(dsn = "data_to_use",
layer = tools::file_path_sans_ext(basename(file_list[9])))## OGR data source with driver: ESRI Shapefile
## Source: "/Users/Franciscorowe 1/Dropbox/Francisco/Research/in_progress/EU paper/interactive_map/EU_map/data_to_use", layer: "FIN_2011_336"
## with 336 features
## It has 11 fields
## Integer64 fields read as strings: 7_popGER <- readOGR(dsn = "data_to_use",
layer = tools::file_path_sans_ext(basename(file_list[10])))## OGR data source with driver: ESRI Shapefile
## Source: "/Users/Franciscorowe 1/Dropbox/Francisco/Research/in_progress/EU paper/interactive_map/EU_map/data_to_use", layer: "GER_2009_412"
## with 412 features
## It has 9 fieldsGRE <- readOGR(dsn = "data_to_use",
layer = tools::file_path_sans_ext(basename(file_list[11])))## OGR data source with driver: ESRI Shapefile
## Source: "/Users/Franciscorowe 1/Dropbox/Francisco/Research/in_progress/EU paper/interactive_map/EU_map/data_to_use", layer: "GRE_2011_54"
## with 54 features
## It has 7 fieldsHUN <- readOGR(dsn = "data_to_use",
layer = tools::file_path_sans_ext(basename(file_list[12])))## OGR data source with driver: ESRI Shapefile
## Source: "/Users/Franciscorowe 1/Dropbox/Francisco/Research/in_progress/EU paper/interactive_map/EU_map/data_to_use", layer: "HUN_2010_168"
## with 168 features
## It has 14 fields
## Integer64 fields read as strings: ID_0 ID_1 ID_2ITA <- readOGR(dsn = "data_to_use",
layer = tools::file_path_sans_ext(basename(file_list[13])))## OGR data source with driver: ESRI Shapefile
## Source: "/Users/Franciscorowe 1/Dropbox/Francisco/Research/in_progress/EU paper/interactive_map/EU_map/data_to_use", layer: "ITA_2009_107"
## with 107 features
## It has 7 fieldsLTU <- readOGR(dsn = "data_to_use",
layer = tools::file_path_sans_ext(basename(file_list[14])))## OGR data source with driver: ESRI Shapefile
## Source: "/Users/Franciscorowe 1/Dropbox/Francisco/Research/in_progress/EU paper/interactive_map/EU_map/data_to_use", layer: "LTU_2010_58"
## with 48 features
## It has 16 fields
## Integer64 fields read as strings: ID_0 ID_1 ID_2 6_2v_popNLD <- readOGR(dsn = "data_to_use",
layer = tools::file_path_sans_ext(basename(file_list[15])))## OGR data source with driver: ESRI Shapefile
## Source: "/Users/Franciscorowe 1/Dropbox/Francisco/Research/in_progress/EU paper/interactive_map/EU_map/data_to_use", layer: "NLD_2010_431_2"
## with 431 features
## It has 13 fields
## Integer64 fields read as strings: AANT_INW OPP_TOT OPP_LAND OPP_WATER 8_popNOR <- readOGR(dsn = "data_to_use",
layer = tools::file_path_sans_ext(basename(file_list[16])))## OGR data source with driver: ESRI Shapefile
## Source: "/Users/Franciscorowe 1/Dropbox/Francisco/Research/in_progress/EU paper/interactive_map/EU_map/data_to_use", layer: "NOR_2013_428"
## with 428 features
## It has 15 fields
## Integer64 fields read as strings: 5_2_popPOL <- readOGR(dsn = "data_to_use",
layer = tools::file_path_sans_ext(basename(file_list[17])))## OGR data source with driver: ESRI Shapefile
## Source: "/Users/Franciscorowe 1/Dropbox/Francisco/Research/in_progress/EU paper/interactive_map/EU_map/data_to_use", layer: "POL_2010_376_2"
## with 376 features
## It has 16 fields
## Integer64 fields read as strings: ID_0 ID_1 ID_2POR <- readOGR(dsn = "data_to_use",
layer = tools::file_path_sans_ext(basename(file_list[18])))## OGR data source with driver: ESRI Shapefile
## Source: "/Users/Franciscorowe 1/Dropbox/Francisco/Research/in_progress/EU paper/interactive_map/EU_map/data_to_use", layer: "POR_2011_30"
## with 30 features
## It has 11 fieldsROU <- readOGR(dsn = "data_to_use",
layer = tools::file_path_sans_ext(basename(file_list[19])))## OGR data source with driver: ESRI Shapefile
## Source: "/Users/Franciscorowe 1/Dropbox/Francisco/Research/in_progress/EU paper/interactive_map/EU_map/data_to_use", layer: "ROU_2002_42"
## with 42 features
## It has 10 fieldsRUS <- readOGR(dsn = "data_to_use",
layer = tools::file_path_sans_ext(basename(file_list[20])))## OGR data source with driver: ESRI Shapefile
## Source: "/Users/Franciscorowe 1/Dropbox/Francisco/Research/in_progress/EU paper/interactive_map/EU_map/data_to_use", layer: "RUS_2010_80_2"
## with 80 features
## It has 8 fieldsSPA <- readOGR(dsn = "data_to_use",
layer = tools::file_path_sans_ext(basename(file_list[21])))## OGR data source with driver: ESRI Shapefile
## Source: "/Users/Franciscorowe 1/Dropbox/Francisco/Research/in_progress/EU paper/interactive_map/EU_map/data_to_use", layer: "SPA_2011_52"
## with 52 features
## It has 8 fieldsSWE <- readOGR(dsn = "data_to_use",
layer = tools::file_path_sans_ext(basename(file_list[22])))## OGR data source with driver: ESRI Shapefile
## Source: "/Users/Franciscorowe 1/Dropbox/Francisco/Research/in_progress/EU paper/interactive_map/EU_map/data_to_use", layer: "SWE_2012_290"
## with 290 features
## It has 9 fields
## Integer64 fields read as strings: 1_2_popTUR <- readOGR(dsn = "data_to_use",
layer = tools::file_path_sans_ext(basename(file_list[23])))## OGR data source with driver: ESRI Shapefile
## Source: "/Users/Franciscorowe 1/Dropbox/Francisco/Research/in_progress/EU paper/interactive_map/EU_map/data_to_use", layer: "TUR_2012_81"
## with 81 features
## It has 7 fieldsUK <- readOGR(dsn = "data_to_use",
layer = tools::file_path_sans_ext(basename(file_list[24])))## OGR data source with driver: ESRI Shapefile
## Source: "/Users/Franciscorowe 1/Dropbox/Francisco/Research/in_progress/EU paper/interactive_map/EU_map/data_to_use", layer: "UK_2011_404"
## with 404 features
## It has 12 fields
## Integer64 fields read as strings: CodeID uk_pop# Print the names of the columns
str(AUT@data)## 'data.frame': 99 obs. of 7 variables:
## $ name : Factor w/ 99 levels "Amstetten","Baden",..: 12 11 24 33 52 59 60 61 67 15 ...
## $ CodeID : num 1 3 4 5 6 7 8 9 2 19 ...
## $ X3_2_pop : num 12850 40624 26448 17483 38976 ...
## $ X3_2_nmr : num 0.599 0.633 -0.31 0.143 0.423 ...
## $ X3_2_ln_pop: num 2.48 1.96 1.74 1.85 2.24 ...
## $ X3_2_countr: Factor w/ 1 level "Austria": 1 1 1 1 1 1 1 1 1 1 ...
## $ X3_2_z_nmr : num 1.602 1.679 -0.507 0.544 1.194 ...# Select the columns that we are interested in
AUT@data <- AUT@data[,c("name", "X3_2_nmr", "X3_2_z_nmr")]
# Rename the columns so there are consistent across countries
names(AUT@data) <- c("name", "net_migr", "z_score")# Print the names of the columns
str(BEL@data)## 'data.frame': 589 obs. of 8 variables:
## $ name : Factor w/ 589 levels "Aalst","Aalter",..: 18 4 67 69 73 82 83 141 152 220 ...
## $ CodeSTR : num 11002 11001 11004 11005 11007 ...
## $ Code : int 2 1 3 4 5 6 7 8 9 10 ...
## $ X9_3_nmr : num -0.889 0.621 0.606 1.401 -1.07 ...
## $ X9_3_pop : Factor w/ 584 levels "10010","10018",..: 388 121 67 148 10 354 278 232 164 574 ...
## $ X9_3_ln_pop: num 3.35 3.1 2.77 3.34 3.41 ...
## $ X9_3_countr: Factor w/ 1 level "Belgium": 1 1 1 1 1 1 1 1 1 1 ...
## $ X9_3_z_nmr : num -1.659 0.465 0.443 1.561 -1.913 ...# Select the columns that we are interested in
BEL@data <- BEL@data[,c("name", "X9_3_nmr", "X9_3_z_nmr")]
# Rename the columns so there are consistent across countries
names(BEL@data) <- c("name", "net_migr", "z_score")# Print the names of the columns
str(BRL@data)## 'data.frame': 118 obs. of 20 variables:
## $ GID_0 : Factor w/ 1 level "BLR": 1 1 1 1 1 1 1 1 1 1 ...
## $ NAME_0 : Factor w/ 1 level "Belarus": 1 1 1 1 1 1 1 1 1 1 ...
## $ GID_1 : Factor w/ 6 levels "BLR.1_1","BLR.2_1",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ NAME_1 : Factor w/ 6 levels "Brest","Homyel'",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ NL_NAME_1 : Factor w/ 0 levels: NA NA NA NA NA NA NA NA NA NA ...
## $ GID_2 : Factor w/ 118 levels "BLR.1.1_1","BLR.1.10_1",..: 1 9 10 11 12 13 14 15 16 2 ...
## $ NAME_2 : Factor w/ 118 levels "Aktsyabar","Arsha",..: 7 10 13 26 31 38 39 42 53 61 ...
## $ VARNAME_2 : Factor w/ 118 levels "Baranovichy",..: 1 4 11 22 26 33 34 37 44 59 ...
## $ NL_NAME_2 : Factor w/ 17 levels "Bykhov","Mosty",..: NA NA NA NA NA NA NA NA NA NA ...
## $ TYPE_2 : Factor w/ 1 level "Raion": 1 1 1 1 1 1 1 1 1 1 ...
## $ ENGTYPE_2 : Factor w/ 1 level "District": 1 1 1 1 1 1 1 1 1 1 ...
## $ CC_2 : Factor w/ 0 levels: NA NA NA NA NA NA NA NA NA NA ...
## $ HASC_2 : Factor w/ 117 levels "BY.BR.BA","BY.BR.BR",..: 1 3 2 4 5 6 7 9 8 11 ...
## $ BLR_area : num 2258 1413 1690 1855 1710 ...
## $ X1_pop : Factor w/ 118 levels "10047","102859",..: 40 108 83 93 70 95 104 84 116 112 ...
## $ X1_nmr : num 0.336 -0.533 0.936 -1.242 -0.631 ...
## $ X1_ln_popde: num 4.76 1.67 4.79 1.35 1.25 ...
## $ X1_bsu_name: Factor w/ 118 levels "Baranovichy",..: 1 4 11 22 26 33 34 37 44 59 ...
## $ X1_l_area : Factor w/ 1 level "2257.89": 1 NA NA NA NA NA NA NA NA NA ...
## $ X1_z_nmr : num 1.2467 0.1409 1.7234 -0.7134 0.0229 ...# Select the columns that we are interested in
BRL@data <- BRL@data[,c("NAME_2", "X1_nmr", "X1_z_nmr")]
# Rename the columns so there are consistent across countries
names(BRL@data) <- c("name", "net_migr", "z_score")# Print the names of the columns
str(BUL@data)## 'data.frame': 263 obs. of 15 variables:
## $ ID_0 : Factor w/ 1 level "37": 1 1 1 1 1 1 1 1 1 1 ...
## $ ISO : Factor w/ 1 level "BGR": 1 1 1 1 1 1 1 1 1 1 ...
## $ NAME_0 : Factor w/ 1 level "Bulgaria": 1 1 1 1 1 1 1 1 1 1 ...
## $ ID_1 : Factor w/ 28 levels "1","10","11",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ NAME_1 : Factor w/ 29 levels "Blagoevgrad",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ ID_2 : Factor w/ 263 levels "1","10","100",..: 1 112 187 198 209 220 231 242 253 2 ...
## $ NAME_2 : Factor w/ 262 levels "Aksakovo","Alfatar",..: 12 15 20 72 79 83 113 165 181 193 ...
## $ TYPE_2 : Factor w/ 2 levels "City","Obshtina": 2 2 1 2 2 2 2 2 2 2 ...
## $ ENGTYPE_2 : Factor w/ 2 levels "City","Municipality": 2 2 1 2 2 2 2 2 2 2 ...
## $ NL_NAME_2 : Factor w/ 189 levels "Айтос","Аксаково",..: 10 13 18 46 43 173 84 121 133 141 ...
## $ VARNAME_2 : Factor w/ 56 levels "Accadânalar|Akkadınlar",..: NA NA 19 NA 18 NA NA NA NA 47 ...
## $ X5_pop : num 13005 9503 76859 14528 31915 ...
## $ X5_nmr : num -1.18 -0.78 -1.49 -1.18 -0.71 -0.71 -0.52 -0.81 -0.22 -0.35 ...
## $ X5_ln_popde: num 1.44 1.51 2.09 1.57 1.99 1.5 1.23 1.94 1.69 1.63 ...
## $ X5_z_nmr : num -1.324 -0.726 -1.787 -1.324 -0.621 ...# Select the columns that we are interested in
BUL@data <- BUL@data[,c("NAME_2", "X5_nmr", "X5_z_nmr")]
# Rename the columns so there are consistent across countries
names(BUL@data) <- c("name", "net_migr", "z_score")# Print the names of the columns
str(CHE@data)## 'data.frame': 184 obs. of 13 variables:
## $ BEZIRK : int 1801 2303 1405 1903 213 503 1814 1401 900 215 ...
## $ KT : int 18 23 14 19 2 5 18 14 9 2 ...
## $ NAME : Factor w/ 184 levels "Amt Aarberg",..: 33 139 103 41 14 59 114 82 184 154 ...
## $ District : num 1801 2303 1405 1903 213 ...
## $ Code : num 102 164 82 118 25 47 115 78 54 27 ...
## $ PAR : num 8064 10793 4473 54775 12890 ...
## $ X2_pop : Factor w/ 184 levels "103702","10414",..: 169 5 131 145 20 49 165 121 170 142 ...
## $ X2_nmr : num -5.32 -1.455 -1.878 0.783 0.566 ...
## $ X2_ln_popde: num 1.05 1.23 2.17 2.67 2.16 ...
## $ X2_bsu_name: Factor w/ 184 levels "Amt Aarberg",..: 33 139 103 41 14 59 114 82 184 154 ...
## $ X2_country : Factor w/ 1 level "Switzerland": 1 1 1 1 1 1 1 1 1 1 ...
## $ X2_l_area : num 724.2 633.8 30.2 117.8 88.2 ...
## $ X2_z_nmr : num -1.792 -0.345 -0.503 0.493 0.412 ...# Select the columns that we are interested in
CHE@data <- CHE@data[,c("NAME", "X2_nmr", "X2_z_nmr")]
# Rename the columns so there are consistent across countries
names(CHE@data) <- c("name", "net_migr", "z_score")# Print the names of the columns
str(CZE@data)## 'data.frame': 77 obs. of 7 variables:
## $ Name : Factor w/ 77 levels "Beneov","Beroun",..: 8 9 26 57 51 65 68 4 3 6 ...
## $ Code : num 14 15 16 17 18 19 20 59 56 58 ...
## $ X6_3_nmr : num 0.3381 -0.2886 -0.0215 0.1359 -0.3514 ...
## $ X6_3_pop : num 187240 61671 93223 70632 51511 ...
## $ X6_3_ln_pop: num 2.06 1.61 1.68 1.76 1.61 ...
## $ X6_3_countr: Factor w/ 1 level "Czech Republic": 1 1 1 1 1 1 1 1 1 1 ...
## $ X6_3_z_nmr : num 0.465 -0.645 -0.172 0.107 -0.757 ...# Select the columns that we are interested in
CZE@data <- CZE@data[,c("Name", "X6_3_nmr", "X6_3_z_nmr")]
# Rename the columns so there are consistent across countries
names(CZE@data) <- c("name", "net_migr", "z_score")# Print the names of the columns
str(DNK@data)## 'data.frame': 99 obs. of 7 variables:
## $ name : Factor w/ 99 levels "Aabenraa","Aalborg",..: 4 5 6 7 8 64 9 10 11 14 ...
## $ CodeID : num 5 18 48 6 58 53 29 7 89 3 ...
## $ X4_pop : Factor w/ 99 levels "105","107618",..: 24 19 50 64 21 27 49 34 36 5 ...
## $ X4_nmr : num -0.206 -0.133 -0.512 0.196 -0.514 ...
## $ X4_ln_popde: num 3.04 2.55 1.91 3.17 1.69 ...
## $ X4_country : Factor w/ 1 level "Denmark": 1 1 1 1 1 1 1 1 1 1 ...
## $ X4_z_nmr : num -0.1462 -0.0675 -0.4771 0.2875 -0.4791 ...# Select the columns that we are interested in
DNK@data <- DNK@data[,c("name", "X4_nmr", "X4_z_nmr")]
# Rename the columns so there are consistent across countries
names(DNK@data) <- c("name", "net_migr", "z_score")# Print the names of the columns
str(EST@data)## 'data.frame': 223 obs. of 16 variables:
## $ ID_0 : Factor w/ 1 level "73": 1 1 1 1 1 1 1 1 1 1 ...
## $ ISO : Factor w/ 1 level "EST": 1 1 1 1 1 1 1 1 1 1 ...
## $ NAME_0 : Factor w/ 1 level "Estonia": 1 1 1 1 1 1 1 1 1 1 ...
## $ ID_1 : Factor w/ 16 levels "1","10","11",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ NAME_1 : Factor w/ 16 levels "Harju","Hiiu",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ ID_2 : Factor w/ 223 levels "1","10","100",..: 1 112 147 158 169 180 191 202 213 2 ...
## $ NAME_2 : Factor w/ 222 levels "Abja","Aegviidu",..: 2 8 24 32 73 53 54 57 72 77 ...
## $ TYPE_2 : Factor w/ 3 levels "Linn","Vald",..: 2 2 2 2 2 2 2 2 2 2 ...
## $ ENGTYPE_2 : Factor w/ 3 levels "Parish","Town",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ NL_NAME_2 : Factor w/ 0 levels: NA NA NA NA NA NA NA NA NA NA ...
## $ VARNAME_2 : Factor w/ 1 level "Lake Võrtsjärv": NA NA NA NA NA NA NA NA NA NA ...
## $ X7_2v_pop : Factor w/ 195 levels "10000","1005",..: 171 149 37 154 51 137 86 135 148 161 ...
## $ X7_2v_nmr : num -1.039 -1.325 6.328 0.553 -2.089 ...
## $ X7_2v_ln_po: num 1.811 1.058 1.929 1.478 0.728 ...
## $ X7_2v_count: Factor w/ 1 level "Estonia": 1 1 1 1 1 1 1 1 1 1 ...
## $ X7_2v_z_nmr: num -0.169 -0.272 2.477 0.403 -0.546 ...# Select the columns that we are interested in
EST@data <- EST@data[,c("NAME_2", "X7_2v_nmr", "X7_2v_z_nmr")]
# Rename the columns so there are consistent across countries
names(EST@data) <- c("name", "net_migr", "z_score")# Print the names of the columns
str(FIN@data)## 'data.frame': 336 obs. of 11 variables:
## $ Name : Factor w/ 336 levels "Akaa","Alajrvi",..: 280 157 9 25 146 16 64 160 330 282 ...
## $ CodeSTR : Factor w/ 336 levels "005","009","010",..: 275 157 9 25 147 18 61 160 327 277 ...
## $ Code : num 277 156 9 25 145 18 61 159 327 279 ...
## $ CodeInt : int 277 156 9 25 145 18 61 159 327 279 ...
## $ X7_pop : Factor w/ 332 levels "1004","101948",..: 14 47 327 44 69 250 200 193 207 3 ...
## $ X7_nmr : num -9.91 -0.161 0.938 -0.659 1.796 ...
## $ X7_ln_popde: num -0.49 0.949 0.104 0.092 0.278 ...
## $ X7_bsu_name: Factor w/ 336 levels "Akaa","Alaj�rvi",..: 277 156 9 25 145 18 61 159 328 279 ...
## $ X7_country : Factor w/ 1 level "Finland": 1 1 1 1 1 1 1 1 1 1 ...
## $ X7_l_area : num 343 1750 755 1227 968 ...
## $ X7_z_nmr : num -9.885 0.22 1.358 -0.297 2.248 ...# Select the columns that we are interested in
FIN@data <- FIN@data[,c("Name", "X7_nmr", "X7_z_nmr")]
# Rename the columns so there are consistent across countries
names(FIN@data) <- c("name", "net_migr", "z_score")# Print the names of the columns
str(GER@data)## 'data.frame': 412 obs. of 9 variables:
## $ Name : Factor w/ 412 levels "Ahrweiler","Aichach-Friedberg",..: 1 2 3 4 5 6 7 8 9 10 ...
## $ NUTS3 : Factor w/ 412 levels "DE111","DE112",..: 307 131 40 410 308 364 48 332 83 80 ...
## $ CodeSTR : Factor w/ 412 levels "GER_adm2_0001",..: 145 310 219 412 146 379 227 170 262 259 ...
## $ Code : num 145 310 219 412 146 379 227 170 262 259 ...
## $ CodeInt : int 145 310 219 412 146 379 227 170 262 259 ...
## $ X6_pop : num 128286 127788 190143 100960 133329 ...
## $ X6_nmr : num 0.118 0.162 -0.153 -0.887 -0.27 ...
## $ X6_ln_popde: num 2.2 2.21 2.14 2.26 2.31 ...
## $ X6_z_nmr : num 0.471 0.572 -0.15 -1.836 -0.419 ...# Select the columns that we are interested in
GER@data <- GER@data[,c("Name", "X6_nmr", "X6_z_nmr")]
# Rename the columns so there are consistent across countries
names(GER@data) <- c("name", "net_migr", "z_score")# Print the names of the columns
str(GRE@data)## 'data.frame': 54 obs. of 7 variables:
## $ Name : Factor w/ 54 levels "Achaia","Argolida",..: 9 12 24 46 53 1 10 18 14 17 ...
## $ Code : num 27 39 30 41 40 9 1 10 37 26 ...
## $ X8_3_nmr : num 0.4264 0.3922 -0.0372 0.2801 -0.0646 ...
## $ X8_3_pop : num 93810 137690 134320 107120 108290 ...
## $ X8_3_ln_pop: num 1.4 1.52 1.84 1.62 1.77 ...
## $ X8_3_countr: Factor w/ 1 level "Greece": 1 1 1 1 1 1 1 1 1 1 ...
## $ X8_3_z_nmr : num 0.1164 0.0492 -0.7939 -0.1709 -0.8477 ...# Select the columns that we are interested in
GRE@data <- GRE@data[,c("Name", "X8_3_nmr", "X8_3_z_nmr")]
# Rename the columns so there are consistent across countries
names(GRE@data) <- c("name", "net_migr", "z_score")# Print the names of the columns
str(HUN@data)## 'data.frame': 168 obs. of 14 variables:
## $ ID_0 : Factor w/ 1 level "103": 1 1 1 1 1 1 1 1 1 1 ...
## $ ISO : Factor w/ 1 level "HUN": 1 1 1 1 1 1 1 1 1 1 ...
## $ NAME_0 : Factor w/ 1 level "Hungary": 1 1 1 1 1 1 1 1 1 1 ...
## $ ID_1 : Factor w/ 20 levels "1","10","11",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ NAME_1 : Factor w/ 20 levels "Bács-Kiskun",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ ID_2 : Factor w/ 168 levels "1","10","100",..: 1 81 92 103 114 125 136 147 158 2 ...
## $ NAME_2 : Factor w/ 168 levels "Abai","Abauj-Hegykozi",..: 6 7 62 65 69 71 72 73 74 82 ...
## $ TYPE_2 : Factor w/ 1 level "Kistérség": 1 1 1 1 1 1 1 1 1 1 ...
## $ ENGTYPE_2 : Factor w/ 1 level "Subregion": 1 1 1 1 1 1 1 1 1 1 ...
## $ X4_2_pop : num 16631 73426 16114 51354 173236 ...
## $ X4_2_nmr : num -0.34 -0.15 -0.34 -0.43 0.1 -0.17 -0.36 -0.28 -0.14 -0.26 ...
## $ X4_2_ln_pop: num 1.64 1.79 1.61 1.7 2.07 ...
## $ X4_2_countr: Factor w/ 1 level "Hungary": 1 1 1 1 1 1 1 1 1 1 ...
## $ X4_2_z_nmr : num -0.647 -0.173 -0.647 -0.871 0.45 ...# Select the columns that we are interested in
HUN@data <- HUN@data[,c("NAME_2", "X4_2_nmr", "X4_2_z_nmr")]
# Rename the columns so there are consistent across countries
names(HUN@data) <- c("name", "net_migr", "z_score")# Print the names of the columns
str(ITA@data)## 'data.frame': 107 obs. of 7 variables:
## $ name : Factor w/ 107 levels "Agrigento","Alessandria",..: 93 103 58 29 6 2 99 39 86 36 ...
## $ code : num 1 2 3 4 5 6 7 8 9 10 ...
## $ X9_2_pop : num 2300000 179955 367672 587803 220654 ...
## $ X9_2_nmr : num -0.02319 -0.00389 0.24261 0.17693 0.11194 ...
## $ X9_2_ln_pop: num 2.53 1.94 2.44 1.93 2.16 ...
## $ X9_2_countr: Factor w/ 1 level "Italy": 1 1 1 1 1 1 1 1 1 1 ...
## $ X9_2_z_nmr : num -0.1826 -0.0932 1.0495 0.745 0.4438 ...# Select the columns that we are interested in
ITA@data <- ITA@data[,c("name", "X9_2_nmr", "X9_2_z_nmr")]
# Rename the columns so there are consistent across countries
names(ITA@data) <- c("name", "net_migr", "z_score")# Print the names of the columns
str(LTU@data)## 'data.frame': 48 obs. of 16 variables:
## $ ID_0 : Factor w/ 1 level "130": 1 1 1 1 1 1 1 1 1 1 ...
## $ ISO : Factor w/ 1 level "LTU": 1 1 1 1 1 1 1 1 1 1 ...
## $ NAME_0 : Factor w/ 1 level "Lithuania": 1 1 1 1 1 1 1 1 1 1 ...
## $ ID_1 : Factor w/ 10 levels "1","10","2","3",..: 1 1 1 1 1 1 3 3 3 3 ...
## $ NAME_1 : Factor w/ 10 levels "Alytaus","Kauno",..: 6 6 6 6 6 6 1 1 1 1 ...
## $ ID_2 : Factor w/ 48 levels "1","10","11",..: 1 12 23 34 44 45 46 47 48 2 ...
## $ NAME_2 : Factor w/ 48 levels "Akmenes","Alytaus",..: 34 1 9 14 23 29 2 6 18 45 ...
## $ TYPE_2 : Factor w/ 3 levels "Miesto Savivaldybe",..: 1 2 2 2 2 2 2 3 2 2 ...
## $ ENGTYPE_2 : Factor w/ 3 levels "City Municipality",..: 1 2 2 2 2 2 2 3 2 2 ...
## $ NL_NAME_2 : Factor w/ 0 levels: NA NA NA NA NA NA NA NA NA NA ...
## $ VARNAME_2 : Factor w/ 48 levels "Akmene|Akmene",..: 34 1 9 14 23 29 2 6 18 45 ...
## $ X6_2v_pop : Factor w/ 48 levels "17478","18429",..: 37 11 13 25 10 36 16 8 9 12 ...
## $ X6_2v_nmr : num -0.51 -0.86 -0.49 -0.58 -0.74 -0.41 0.52 -0.18 -0.2 -0.32 ...
## $ X6_2v_ln_po: num 1.43 1.49 1.4 1.33 1.3 1.46 1.33 1.72 1.26 1.09 ...
## $ X6_2v_count: Factor w/ 1 level "Lithuania": 1 1 1 1 1 1 1 1 1 1 ...
## $ X6_2v_z_nmr: num -0.516 -0.913 -0.493 -0.595 -0.776 ...# Select the columns that we are interested in
LTU@data <- LTU@data[,c("NAME_2", "X6_2v_nmr", "X6_2v_z_nmr")]
# Rename the columns so there are consistent across countries
names(LTU@data) <- c("name", "net_migr", "z_score")# Print the names of the columns
str(NLD@data)## 'data.frame': 431 obs. of 13 variables:
## $ GM_CODE : Factor w/ 431 levels "GM0003","GM0005",..: 1 2 3 4 5 6 7 8 9 10 ...
## $ GM_NAAM : Factor w/ 431 levels "'s-Gravenhage",..: 23 31 35 343 85 136 137 148 173 196 ...
## $ AANT_INW : Factor w/ 421 levels "100300","10235",..: 37 6 402 375 220 128 39 125 274 139 ...
## $ OPP_TOT : Factor w/ 425 levels "10048","1011",..: 159 264 24 268 143 387 398 286 357 326 ...
## $ OPP_LAND : Factor w/ 428 levels "10027","10105",..: 159 260 20 265 55 371 400 267 338 323 ...
## $ OPP_WATER : Factor w/ 343 levels "-99999998","0",..: 318 221 100 237 335 271 337 260 286 337 ...
## $ Name : Factor w/ 431 levels "'s-Gravenhage (gemeente)",..: 23 31 35 343 85 136 137 148 173 196 ...
## $ Code : num 8 11 1 21 9 14 15 16 17 18 ...
## $ X8_pop : Factor w/ 421 levels "100300","10235",..: 37 6 402 375 220 128 39 125 274 139 ...
## $ X8_nmr : num 0.133 0.0766 0.27 0.2424 -1.1639 ...
## $ X8_ln_popde: num 2.69 2.37 1.92 2.21 2.29 ...
## $ X8_country : Factor w/ 1 level "Netherlands": 1 1 1 1 1 1 1 1 1 1 ...
## $ X8_z_nmr : num 0.388 0.288 0.632 0.583 -1.925 ...# Select the columns that we are interested in
NLD@data <- NLD@data[,c("Name", "X8_nmr", "X8_z_nmr")]
# Rename the columns so there are consistent across countries
names(NLD@data) <- c("name", "net_migr", "z_score")# Print the names of the columns
str(NOR@data)## 'data.frame': 428 obs. of 15 variables:
## $ OBJTYPE : Factor w/ 1 level "Kommune": 1 1 1 1 1 1 1 1 1 1 ...
## $ NAVN : Factor w/ 422 levels "Agdenes","Alstahaug",..: 1 2 3 4 5 6 7 8 9 10 ...
## $ KOMM : int 1622 1820 2012 438 719 1871 118 906 220 124 ...
## $ Shape_Leng : num 79650 126931 610991 152465 60957 ...
## $ Shape_Area : num 3.19e+08 1.55e+08 3.77e+09 9.47e+08 1.86e+08 ...
## $ kommnr : Factor w/ 428 levels "0101","0104",..: 300 350 414 61 120 384 6 145 26 11 ...
## $ fylke : Factor w/ 19 levels "01","02","03",..: 15 17 19 4 7 17 1 9 2 1 ...
## $ Origin : Factor w/ 428 levels "0101 Halden",..: 300 350 414 61 120 384 6 145 26 11 ...
## $ Code : num 300 350 414 61 120 384 6 145 26 11 ...
## $ Name : Factor w/ 422 levels "Agdenes","Alstahaug",..: 1 2 3 4 5 6 7 8 9 10 ...
## $ X5_2_pop : Factor w/ 414 levels "10029","1003",..: 103 369 125 171 321 300 71 272 326 86 ...
## $ X5_2_nmr : num 1.042 -1.369 -0.649 -0.779 1.935 ...
## $ X5_2_ln_pop: num 0.734 1.676 0.719 0.411 1.481 ...
## $ X5_2_countr: Factor w/ 1 level "Norway": 1 1 1 1 1 1 1 1 1 1 ...
## $ X5_2_z_nmr : num 1.148 -0.81 -0.225 -0.33 1.874 ...# Select the columns that we are interested in
NOR@data <- NOR@data[,c("Name", "X5_2_nmr", "X5_2_z_nmr")]
# Rename the columns so there are consistent across countries
names(NOR@data) <- c("name", "net_migr", "z_score")# Print the names of the columns
str(POL@data)## 'data.frame': 376 obs. of 16 variables:
## $ ID_0 : Factor w/ 1 level "181": 1 1 1 1 1 1 1 1 1 1 ...
## $ ISO : Factor w/ 1 level "POL": 1 1 1 1 1 1 1 1 1 1 ...
## $ NAME_0 : Factor w/ 1 level "Poland": 1 1 1 1 1 1 1 1 1 1 ...
## $ ID_1 : Factor w/ 16 levels "1","10","11",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ NAME_1 : Factor w/ 16 levels "Greater Poland",..: 4 4 4 4 4 4 4 4 4 4 ...
## $ ID_2 : Factor w/ 376 levels "1","10","100",..: 1 112 223 311 322 333 344 355 366 2 ...
## $ NAME_2 : Factor w/ 376 levels "Aleksandrów",..: 149 148 133 136 153 5 24 130 203 215 ...
## $ TYPE_2 : Factor w/ 1 level "Powiat": 1 1 1 1 1 1 1 1 1 1 ...
## $ ENGTYPE_2 : Factor w/ 2 levels "City","County": 2 1 2 2 2 2 2 2 2 2 ...
## $ NL_NAME_2 : Factor w/ 0 levels: NA NA NA NA NA NA NA NA NA NA ...
## $ VARNAME_2 : Factor w/ 366 levels "Biala Podlaska",..: 155 26 141 143 158 41 55 138 202 211 ...
## $ X4_3v_nmr : num 0.98 -0.21 0.23 -0.22 -0.17 -0.24 0.32 -0.28 -0.22 0.27 ...
## $ X4_3v_pop : num 67397 736510 50856 52495 81196 ...
## $ X4_3v_ln_po: num 2.13 3.4 1.92 1.83 1.91 2.07 1.93 2.06 1.88 2.39 ...
## $ X4_3v_count: Factor w/ 1 level "Poland": 1 1 1 1 1 1 1 1 1 1 ...
## $ X4_3v_z_nmr: num 2.508 -0.376 0.691 -0.4 -0.279 ...# Select the columns that we are interested in
POL@data <- POL@data[,c("NAME_2", "X4_3v_nmr", "X4_3v_z_nmr")]
# Rename the columns so there are consistent across countries
names(POL@data) <- c("name", "net_migr", "z_score")# Print the names of the columns
str(POR@data)## 'data.frame': 30 obs. of 11 variables:
## $ ADMIN_NAME : Factor w/ 30 levels "Alentejo Central",..: 6 7 11 28 23 9 1 3 24 4 ...
## $ CNTRY_NAME : Factor w/ 1 level "Portugal": 1 1 1 1 1 1 1 1 1 1 ...
## $ SUBR2001 : int 200 300 161 163 111 189 189 150 166 189 ...
## $ CNTRY : int 620 620 620 620 620 620 620 620 620 620 ...
## $ id : int 29 30 10 12 1 27 25 9 20 26 ...
## $ X3_pop : num 242360 258980 340400 227220 257060 ...
## $ X3_nmr : num -0.0908 -0.2394 0.5758 0.088 -0.4513 ...
## $ X3_ln_popde: num 2 2.5 2.28 2.11 2.06 ...
## $ X3_bsu_name: Factor w/ 30 levels "Alentejo Central",..: 28 27 9 26 21 7 4 3 22 1 ...
## $ X3_country : Factor w/ 1 level "Portugal": 1 1 1 1 1 1 1 1 1 1 ...
## $ X3_z_nmr : num 0.2373 0.0528 1.0644 0.4591 -0.2101 ...# Select the columns that we are interested in
POR@data <- POR@data[,c("ADMIN_NAME", "X3_nmr", "X3_z_nmr")]
# Rename the columns so there are consistent across countries
names(POR@data) <- c("name", "net_migr", "z_score")# Print the names of the columns
str(ROU@data)## 'data.frame': 42 obs. of 10 variables:
## $ NUTS3 : Factor w/ 42 levels "RO111","RO112",..: 14 5 4 17 2 1 15 6 3 16 ...
## $ Name : Factor w/ 42 levels "RO111 / Bihor",..: 14 5 4 17 2 1 15 6 3 16 ...
## $ Code : num 14 5 4 17 2 1 15 6 3 16 ...
## $ Area : num 5032 4431 6307 8542 5340 ...
## $ X5_3_nmr : num 0.0394 -0.1146 0.2961 0.1005 0.021 ...
## $ X5_3_pop : num 462078 374461 520724 706591 318678 ...
## $ X5_3_ln_pop: num 1.96 1.93 1.92 1.92 1.78 ...
## $ X5_3_bsu_na: Factor w/ 42 levels "Alba","Arad",..: 7 34 27 36 6 5 25 33 14 30 ...
## $ X5_3_countr: Factor w/ 1 level "Romania": 1 1 1 1 1 1 1 1 1 1 ...
## $ X5_3_z_nmr : num 0.1581 -0.8275 1.8019 0.5493 0.0406 ...# Select the columns that we are interested in
ROU@data <- ROU@data[,c("Name", "X5_3_nmr", "X5_3_z_nmr")]
# Rename the columns so there are consistent across countries
names(ROU@data) <- c("name", "net_migr", "z_score")# Print the names of the columns
str(RUS@data)## 'data.frame': 80 obs. of 8 variables:
## $ VARNAME_1 : Factor w/ 79 levels "Adygea|Adygeya|Adygheya|Republic of Adygeya|Adygeyskaya A.Obl.|Respublika Adygeya",..: 28 29 30 32 33 34 77 35 36 15 ...
## $ Name : Factor w/ 80 levels "Altai Republic",..: 19 20 53 21 22 54 23 24 25 26 ...
## $ Code : num 73 34 19 68 75 63 49 20 7 37 ...
## $ X9_pop : num 322412 475598 645650 2800000 1300000 ...
## $ X9_nmr : num -0.6 -0.527 -0.224 -0.121 -0.221 ...
## $ X9_ln_popde: num -0.151 1.498 0.573 1.462 0.237 ...
## $ X9_country : Factor w/ 1 level "Russia": 1 1 1 1 1 1 1 1 1 1 ...
## $ X9_z_nmr : num -0.9963 -0.8079 -0.0203 0.2484 -0.0109 ...# Select the columns that we are interested in
RUS@data <- RUS@data[,c("Name", "X9_nmr", "X9_z_nmr")]
# Rename the columns so there are consistent across countries
names(RUS@data) <- c("name", "net_migr", "z_score")# Print the names of the columns
str(SPA@data)## 'data.frame': 52 obs. of 8 variables:
## $ Name : Factor w/ 52 levels "Alava","Albacete",..: 37 41 4 12 17 21 24 26 31 43 ...
## $ Code : num 35 38 4 11 14 18 21 23 29 41 ...
## $ Area : num NA NA NA NA NA NA NA NA NA NA ...
## $ X8_2_pop : num 1100000 981599 675730 1200000 789419 ...
## $ X8_2_nmr : num 0.1585 -0.08863 0.08258 -0.06282 0.00659 ...
## $ X8_2_ln_pop: num 2.41 2.46 1.89 2.22 1.76 ...
## $ X8_2_countr: Factor w/ 1 level "Spain": 1 1 1 1 1 1 1 1 1 1 ...
## $ X8_2_z_nmr : num 0.6506 -0.461 0.3091 -0.3449 -0.0327 ...# Select the columns that we are interested in
SPA@data <- SPA@data[,c("Name", "X8_2_nmr", "X8_2_z_nmr")]
# Rename the columns so there are consistent across countries
names(SPA@data) <- c("name", "net_migr", "z_score")# Print the names of the columns
str(SWE@data)## 'data.frame': 290 obs. of 9 variables:
## $ name : Factor w/ 290 levels "Ale","Alingss",..: 261 268 220 284 92 34 86 21 195 68 ...
## $ CodeSTR : num 114 115 117 120 123 125 126 127 128 136 ...
## $ Code : int 1 2 3 4 5 6 7 8 9 10 ...
## $ area : num 90.3 359.7 258.6 281.6 50.3 ...
## $ X1_2_pop : Factor w/ 290 levels "100029","10040",..: 178 150 175 173 232 130 1 259 85 250 ...
## $ X1_2_nmr : num -0.02472 0.56838 0.20734 0.39857 0.00443 ...
## $ X1_2_ln_pop: num 2.65 1.93 2.19 2.14 3.13 ...
## $ X1_2_countr: Factor w/ 1 level "Sweden": 1 1 1 1 1 1 1 1 1 1 ...
## $ X1_2_z_nmr : num 0.417 1.219 0.73 0.989 0.456 ...# Select the columns that we are interested in
SWE@data <- SWE@data[,c("name", "X1_2_nmr", "X1_2_z_nmr")]
# Rename the columns so there are consistent across countries
names(SWE@data) <- c("name", "net_migr", "z_score")# Print the names of the columns
str(TUR@data)## 'data.frame': 81 obs. of 7 variables:
## $ Name : Factor w/ 81 levels "Adana","Adiyaman",..: 1 2 3 4 5 6 8 10 11 12 ...
## $ Code : num 1 2 3 4 68 5 6 7 75 8 ...
## $ X2_2_pop : num 2100000 594596 701287 553942 379369 ...
## $ X2_2_nmr : num -0.631 -1.391 0.346 -2.731 -0.45 ...
## $ X2_2_ln_pop: num 2.13 1.9 1.68 1.73 1.7 ...
## $ X2_2_countr: Factor w/ 1 level "Turkey": 1 1 1 1 1 1 1 1 1 1 ...
## $ X2_2_z_nmr : num -0.438 -1.106 0.423 -2.286 -0.278 ...# Select the columns that we are interested in
TUR@data <- TUR@data[,c("Name", "X2_2_nmr", "X2_2_z_nmr")]
# Rename the columns so there are consistent across countries
names(TUR@data) <- c("name", "net_migr", "z_score")# Print the names of the columns
str(UK@data)## 'data.frame': 404 obs. of 12 variables:
## $ geo_code : Factor w/ 404 levels "95AA","95BB",..: 397 356 394 195 217 162 10 402 315 183 ...
## $ geo_label : Factor w/ 404 levels "Aberdeen City",..: 275 129 45 200 278 13 83 231 189 317 ...
## $ geo_labelw: Factor w/ 22 levels "Abertawe","Blaenau Gwent",..: 14 NA 12 NA NA NA NA 6 NA NA ...
## $ label : Factor w/ 404 levels "E92000001E06000001",..: 397 356 394 169 191 136 334 402 289 157 ...
## $ name : Factor w/ 404 levels "Aberdeen City",..: 275 129 45 200 278 13 83 231 189 317 ...
## $ CodeID : Factor w/ 404 levels "1","10","100",..: 331 286 328 108 133 72 2 338 242 95 ...
## $ uk_pop : Factor w/ 401 levels "100000","100031",..: 197 227 107 378 282 57 266 117 267 24 ...
## $ uk_nmr : num 0.13 0.04 -0.19 -1.9 -0.46 -0.22 -2.12 0.1 -0.04 0.69 ...
## $ uk_ln_popd: num 2.74 0.93 2.74 3.42 1.6 2.31 1.91 2.82 3.01 2.98 ...
## $ uk_bsu_nam: Factor w/ 404 levels "Aberdeen City",..: 275 129 45 200 278 13 335 231 189 317 ...
## $ uk_country: Factor w/ 1 level "United Kingdom": 1 1 1 1 1 1 1 1 1 1 ...
## $ uk_z_nmr : num 0.17 0.02 -0.34 -3.07 -0.78 -0.39 -3.42 0.12 -0.11 1.05 ...# Select the columns that we are interested in
UK@data <- UK@data[,c("geo_label", "uk_nmr", "uk_z_nmr")]
# Rename the columns so there are consistent across countries
names(UK@data) <- c("name", "net_migr", "z_score")# Use the coordinate system of Netherlands
# The coordinate system is WGS84
identical_crs <- proj4string(NLD)
# and transform Norway to be compatible
NOR <-spTransform(NOR,CRS(identical_crs))# read in the missing countries boundaries
missing <- readOGR(dsn = "data_missing",
layer = "European_countries_modified")## OGR data source with driver: ESRI Shapefile
## Source: "/Users/Franciscorowe 1/Dropbox/Francisco/Research/in_progress/EU paper/interactive_map/EU_map/data_missing", layer: "European_countries_modified"
## with 15 features
## It has 5 fields# Print the names of the columns
str(missing@data)## 'data.frame': 15 obs. of 5 variables:
## $ LEVL_CODE: int 0 0 0 0 0 0 0 0 0 0 ...
## $ NUTS_ID : Factor w/ 15 levels "AL","CY","FR",..: 1 2 3 4 5 6 8 9 10 12 ...
## $ CNTR_CODE: Factor w/ 15 levels "AL","CY","FR",..: 1 2 3 4 5 6 8 9 10 12 ...
## $ NUTS_NAME: Factor w/ 15 levels "FRANCE","HRVATSKA",..: 11 14 1 2 3 4 7 5 15 8 ...
## $ FID : Factor w/ 15 levels "AL","CY","FR",..: 1 2 3 4 5 6 8 9 10 12 ...# create two new columns for net migration and z score to be consistent to the other datasets and fill them with NA values
missing@data$net_migr <- NA
missing@data$z_score <- NA
# Select the columns that we are interested in
missing@data <- missing@data[,c("NUTS_NAME", "net_migr", "z_score")]
# Rename the columns so there are consistent across countries
names(missing@data) <- c("name", "net_migr", "z_score")# Combine all the datasets into one
combined_data <- rbind(AUT, BEL, BRL, BUL, CHE, CZE, DNK, EST, FIN, GER, GRE, HUN, ITA, LTU,
NLD, NOR, POL, POR, ROU, RUS, SPA, SWE, TUR, UK, missing)
# If we want it as a seperate shapefile
# writeOGR(combined_data , ".", "combined_data", driver="ESRI Shapefile")
summary(combined_data@data)## name net_migr z_score
## Neunkirchen: 2 Min. :-26.6667 Min. :-9.884604
## Lier : 2 1st Qu.: -0.4600 1st Qu.:-0.530030
## Zwijndrecht: 2 Median : -0.1029 Median :-0.002988
## Byala : 2 Mean : -0.1419 Mean : 0.000241
## Lom : 2 3rd Qu.: 0.2612 3rd Qu.: 0.516629
## (Other) :4995 Max. : 7.7743 Max. : 8.304665
## NA's : 1 NA's :27 NA's :27library(pryr)## Registered S3 method overwritten by 'pryr':
## method from
## print.bytes Rcpp# check the size of the original shapefile
pryr::object_size(combined_data)## 213 MBlibrary(rgeos)## rgeos version: 0.5-2, (SVN revision 621)
## GEOS runtime version: 3.7.2-CAPI-1.11.2
## Linking to sp version: 1.3-1
## Polygon checking: TRUE# Simplify the shapefile but it does not preserve the columns
combined_data_simpl <- gSimplify(combined_data, tol = 0.001, topologyPreserve=TRUE)
# I create a dataframe with the data from the combined data
combined_data_df <- combined_data@data
# Combine spatial polygons and data
combined_data_simpl <- SpatialPolygonsDataFrame(combined_data_simpl, combined_data_df)
# check the size of the simplified shapefile
pryr::object_size(combined_data_simpl)## 51 MB# categorise data
combined_data_simpl$z_cat <- cut(combined_data_simpl$z_score,
breaks=c(-Inf, -2.0, -0.001, 0.001, 2.0, Inf),
labels=c("less than -2", "-2 to 0", "0", "0 to 2", "more than 2"))library(leaflet)
# Specify the colour palette
pal <- colorFactor(palette = c("red", "salmon1", "white", "steelblue1", "blue"),
levels = c("less than -2", "-2 to 0", "0", "0 to 2", "more than 2"), ordered = FALSE)
# pal <- colorBin("RdYlBu", domain = spdf$z_score, bins = bins)
# There is a list of all the available background tiles in
# http://leaflet-extras.github.io/leaflet-providers/preview/index.html
# Create the initial background map, zooming in Europe
colourmap <- leaflet() %>%
addTiles() %>%
setView(lat = 48, lng = 20, zoom = 4) %>%
addProviderTiles(providers$NASAGIBS.ViirsEarthAtNight2012, group = "NASAGIBS.ViirsEarthAtNight2012") %>%
addProviderTiles(providers$Stamen.TonerLite, group = "Stamen.TonerLite")
# Create the interactive map showing the sequence clusters
interactive_map <- colourmap %>%
addPolygons(data = combined_data_simpl,
fillColor = ~pal(z_cat),
weight = 0.4,
opacity = 0.8,
color = "black",
dashArray = "3",
fillOpacity = 0.7,
popup = paste("Name: ", combined_data_simpl$name, "<br>",
"Net Migration: ", combined_data_simpl$net_migr, "<br>"),
group = "Net migration in Europe",
highlight = highlightOptions(
weight = 5,
color = "#666",
dashArray = "",
fillOpacity = 0.7,
bringToFront = TRUE)) %>%
addLegend(pal = pal,
values = combined_data_simpl$z_cat,
na.label = "Missing data",
position = "bottomleft",
title = "Net migration in Europe (z-scores)") %>%
addLayersControl(baseGroups = c("NASAGIBS.ViirsEarthAtNight2012", "Stamen.TonerLite"),
overlayGroups = "Net migration in Europe",
options = layersControlOptions(collapsed = FALSE))
interactive_map